The military training community utilizes Simulation-Based Training (SBT) to close the gap between classroom-based training and live combat training. SBT typically includes some combination of live (e.g., real tanks and dismounted infantry), virtual (e.g., a live soldier interacting with a tank simulator) and constructive (e.g., fully or semi-autonomous simulated tanks) entities. The various training methods commonly interact with each other within an LVC (Live, Virtual, Constructive) network architecture.
The primary sensory cue indicator in a visual system simulation is the fidelity or “look” of the environment. Due to the importance of fidelity, understanding the levels of interoperability a system maintains is imperative. Interoperability, succinctly defined, is the ability of multiple systems to find a common ground or work together in a coupled environment. Standardization designs across simulators have been developed to support interoperation. However, the differences in individual image generation software (e.g., rendering engines, polygonalization, thinning) of various manufacturers makes it difficult to produce a standardized “fidelity” between applications. Furthermore, proprietary application information is a key factor that limits standardization due to individual manufacturers permitting database correlation or synthesis but prohibiting uniform image generation processes.
Traditionally, correlation and interoperability between two simulation systems is determined by Terrain Database (TDB) correlation methods and/or human visual inspection. TDB correlation methods choose random, corresponding points within the TDB and then perform a numeric comparison(s) of these points. However, there are limitations to using this prior art method. TDB correlation does not assess the images generated, but instead utilizes the underlying data created by image generators. Therefore, differing, which is often proprietary, polygonalization, thinning and rendering algorithms are used and the differences in hardware and software capabilities are excluded from TDB comparisons. Therefore, while a TDB correlation system may conclude that the two images are correlated, the image that each individual trainee actually observes may be very different, depending upon the differences between the two image generators.
In human visual inspection, a direct comparison of generated images is performed by human inspection and is employed in one of two ways. The first involves the use of a side-by-side viewer to subjectively inspect a particular location of interest. Alternatively, in human visual inspection, a human observer may view several, co-located simulation platforms simultaneously to subjectively determine if the visuals presented on each computer display are correlated. However, neither of these approaches objectively measures the rendered images presented to the trainee, nor do they fully explore automated assessment capabilities.
Moreover, it is important to acknowledge the global impact of poor correlation within the LVC (Live, Virtual, Constructive) network architecture paradigm commonly employed in simulation-based training. A trainee operating a virtual asset that communicates with a trainee on the range must also be able to rely upon the validity of his/her visual display to ensure fair fight as well as safety.
Accordingly, what is needed in the art is a system and method capable of objectively assessing rendered images in an automated fashion to identify correlation between the images.